Disentangling human trafficking types and the identification of pathways to forced labor and sex: an explainable analytics approach

Name: Disentangling human trafficking types and the identification of pathways to forced labor and sex: an explainable analytics approach
Author: Eryarsoy, E., Topuz, K., Demiroğlu, Cenk

İsim	Disentangling human trafficking types and the identification of pathways to forced labor and sex: an explainable analytics approach
Yazar	Eryarsoy, E., Topuz, K., Demiroğlu, Cenk
Basım Tarihi:	2023-07
Basım Yeri	- Springer
Konu	Analytics, Bayesian belief networks, Forced labor, Forced sex, Human trafficking, Machine learning
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0254-5330
Kayıt Numarası	7ecd9dad-a101-4c6b-bcc7-67e0e7e2b688
Lokasyon	Electrical & Electronics Engineering
Tarih	2023-07
Örnek Metin	Terms such as human trafficking and modern-day slavery are ephemeral but reflect manifestations of oppression, servitude, and captivity that perpetually have threatened the basic right of all humans. Operations research and analytical tools offering practical wisdom have paid scant attention to this overarching problem. Motivated by this lacuna, this study considers two of the most prevalent categories of human trafficking: forced labor and forced sex. Using one of the largest available datasets due to Counter-Trafficking Data Collective (CTDC), we examine patterns related to forced sex and forced labor. Our study uses a two-phase approach focusing on explainability: Phase 1 involves logistic regression (LR) segueing to association rules analysis and Phase 2 employs Bayesian Belief Networks (BBNs) to uncover intricate pathways leading to human trafficking. This combined approach provides a comprehensive understanding of the factors contributing to human trafficking, effectively addressing the limitations of conventional methods. We confirm and challenge some of the key findings in the extant literature and call for better prevention strategies. Our study goes beyond the pretext of analytics usage by prescribing how to incorporate our results in combating human trafficking.
DOI	10.1007/s10479-023-05520-1

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Disentangling human trafficking types and the identification of pathways to forced labor and sex: an explainable analytics approach

Yazar Eryarsoy, E., Topuz, K., Demiroğlu, Cenk

Basım Tarihi 2023-07

Basım Yeri - Springer

Konu Analytics, Bayesian belief networks, Forced labor, Forced sex, Human trafficking, Machine learning

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0254-5330

Kayıt Numarası 7ecd9dad-a101-4c6b-bcc7-67e0e7e2b688

Lokasyon Electrical & Electronics Engineering

Tarih 2023-07

Örnek Metin Terms such as human trafficking and modern-day slavery are ephemeral but reflect manifestations of oppression, servitude, and captivity that perpetually have threatened the basic right of all humans. Operations research and analytical tools offering practical wisdom have paid scant attention to this overarching problem. Motivated by this lacuna, this study considers two of the most prevalent categories of human trafficking: forced labor and forced sex. Using one of the largest available datasets due to Counter-Trafficking Data Collective (CTDC), we examine patterns related to forced sex and forced labor. Our study uses a two-phase approach focusing on explainability: Phase 1 involves logistic regression (LR) segueing to association rules analysis and Phase 2 employs Bayesian Belief Networks (BBNs) to uncover intricate pathways leading to human trafficking. This combined approach provides a comprehensive understanding of the factors contributing to human trafficking, effectively addressing the limitations of conventional methods. We confirm and challenge some of the key findings in the extant literature and call for better prevention strategies. Our study goes beyond the pretext of analytics usage by prescribing how to incorporate our results in combating human trafficking.

DOI 10.1007/s10479-023-05520-1